Quantitative analysis of electroencephalographic (EEG) signals

Quantitative analysis ofelectroencephalographic (EEG) signals

www.epileptologie-bonn.de

Dept. of EpileptologyUniversity of Bonn

•Quantitative EEG-methods: why?

•Example: Wavelet-based event-related potential (ERP)-analysis

•Phase-locking analysis of mediotemporal lobe (MTL) depth ERPs

•Declarative memory formation: MTL connectivity

•Summary

Quantitative EEG analysis

Quantitative EEG methods: why?Example: sleep-EEG (qualitative)

Rechtschaffen and Kales, 1968

2 Hz

5 Hz

10 Hz

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30 Hz

Time

Hypnogram

Quantitative EEG methods: why?Example: sleep-EEG (qualitative)

Quantitative EEG methods: why?Example: sleep-EEG (quantitative)

Electroencephalogr Clin Neurophysiol 1996; 98: 401-410

Quantitative EEG methods: why?EEG = superposition of oscillations

Visual analysis: only low-frequency oscillations perception, cognitive processes!

1/f amplitude- characteristic

Theta-gamma interaction within hippocampus

Chrobak u. Buzsáki, J. Neurosci. 1998

Interactions(hippocampus):

Theta (5Hz)

Gamma (>30Hz)

Quantitative EEG methods: why?

Quantitative EEG methods: why?Event-related EEG: averaging

Average

event-related

potential (ERP)

Reduction of background „noise“: 1/n

Quantitative EEG methods: why?

Averaged ERP-response ? ?

Amplitude-Changes Phase-Locking

Event-related EEG

evoked induced ( cognition)

Wavelet-based ERP analysisTraditional approach: Fourier-transform

Power density

P () = F () F* ()

Spectral Coherence

Cxy() = |Pxy()|2 Pxx() Pyy()

Fourier-transform

F () = f(t) eit dt

Discrete: Fast-Fourier-transform (FFT)

f = 1 / T !

Wavelet-based ERP analysisPhase-locking vs. amplitude-changes

Morlet-Wavelet: w(t,) = exp(-t2/22) * exp (it) Wavelet-Transform: W(t,) = f(t-) * w(,) d

Power (t,) = W(t,) 2 Phase (t,) = arctan (Im (t,) / Re (t,))

Wavelet-based ERP analysis

Original EEG

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Wavelet-transform (real part)

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uV

* Amplitude/Power (t,)

Phase (t,)

Phase-locking vs. amplitude-changes

WT ERP-responses:


t,1Phases: t,2t,3 ...

Circular variance: | e i |

Shannon entropy: 1 + P log P

Histogram P():

-180 ° 0 ° 180 °

0°-180° 180°

Phase-locking vs. amplitude-changes

Variance?

Phase-locking index

e.g. Lachaux et al., Hum. Brain Mapp. 1999; Tass et al., Phys. Rev. Lett. 1998

Brain region A

Brain region B

t,1 ? t,2 ? t,3 ? t,4 ?

Phase-synchronisation

Variance of phase differences t Synchronisation index


Phase-locking analysis of MTL depth ERPsEpilepsy (prevalence 1%)

Seizures:

Unfamilar sensations

Unvoluntary body movements

Loss of consciousness

Parahippocampal cortex

Rhinal cortex

Amygdala

Hippocampus

Phase-locking analysis of MTL depth ERPsMTL depth-recordings in epilepsy patients

MTL-epilepsy: 45% pharmaco-resistant

Presurgical evaluation: seizure focus?

Memory processes

Phase-locking analysis of MTL depth ERPs

(neue Wörter)„Oddball experiment“: X ... X ... X ... O ... X ... X ... O ... X ... X

Hippocampus sclerosis Non-pathological side

Target Target

Zeit (ms)

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Neuroimage 2005; 24: 980-989

Hippocampal P3


Phase-locking

Power

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cy (

Hz)

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time(ms)

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time(ms)

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Neuroimage 2005; 24: 980-989

Hippocampal P3: low-frequency range


Phase-locking

Power


Neuroimage 2005; 24: 980-989

Hippocampal P3: gamma range

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cy (

Hz)

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0 200 400 600 800 1000time (ms)

(neue Wörter)„Continuous recognition experiment“:

Haus ... Schiff ... Pferd ... Schiff ... Baum ... Haus ... Tisch ...

Old

time (ms)

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am

plitu

de (

µV

) -60

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Correct rejections (new)Hits (old)

OldNewNewNew New New

J. Cogn. Neurosci. 2004; 16:1595-1604

Phase-locking analysis of MTL depth ERPsAnterior mediotemporal lobe (AMTL)-N4

J. Cogn. Neurosci. 2004; 16:1595-1604

Phase-locking

Power

( fMRI)

ERPs

(neue Wörter)(old words) (new words)

Phase-locking analysis of MTL depth ERPsAMTL-N4

Declarative memory formation: MTL connectivityMTL depth electrodes

Interaction?

Declarative long-term memory: Consciously accessible information,

e.g. events and facts

Rhinal Cortex

Convergence of sensory data,semantic preprocessing Hippocampus

Synaptic plasticity, long term potentiation (LTP)

•9 TLE patients with unilateral focus

•“Dm-effect” (difference due to memory):

remembered vs. forgotten words

Subsequent memory paradigm

Sahne

„Uhr“„Appetit“

„Sahne“„Ende“„Leistung“

„Mutter“

87

LearningLearning DistractionDistraction

?

Free recallFree recall

„84“„81“

„78“„75“

„72“. . .

Declarative memory formation MTL connectivity

I I I I I I I I I II2000

µV– 20

400 1600120080020

ms

Rhinal cortex

Hippocampus

remembered

forgotten

Fernández et al., Science 1999

MTL-ERPs: “difference due to memory”

Dm-effects correlated (r = 0.92) rhinal-hippocampal interaction

Direct evidence?

-sync. coupling of assemblies

Declarative memory formation: MTL connectivity

time [s]

Fre

quen

cy [

Hz]

0.0 0.5 1.0 1.532

34

36

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- 30 - 20 - 10 0 + 10 + 20 + 30

Change [%]: remembered - forgotten

Desynchronisation Synchronisation

Nat. Neurosci. 2001; 4: 1259-1264

Rhinal-hippocampal gamma synchronisationDeclarative memory formation: MTL connectivity

-180 ° 0 180 °

0°-180° 180°

Phase-synch. index:

remembered - forgotten

Nat. Neurosci. 2001; 4: 1259-1264

Changes of gamma power

Rhinal cortex

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Changes of gamma power compared to baseline

Hippocampus

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remembered forgotten

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Interpretation

• Rhinal-hippocampal phase coupling initiates information transfer ( 100 ms poststim.)

• Information transfer after onset of rhinal dm-effect (ERPs, 300 ms poststim.)

• Phase decoupling terminates information transfer ( 1000 ms poststim.)

• Reduced gamma power: specific assembly activation, suppression of gamma “noise”


Memory-related theta-gamma cooperation

Sp

ectr

al co

he

ren

ce

[%

] b

etw

ee

nrh

ina

l co

rte

x a

nd

hip

po

ca

mp

us

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forgotten words

remembered words

delta1-4Hz

theta4-7Hz

alpha17-10Hz

alpha210-13Hz

beta113-16Hz

beta216-19Hz

Eur. J. Neurosci. 2003; 17: 1082-1088

"dm"-effect: Gamma-synchronization

-0,2 0,0 0,2 0,4 0,6 0,8 1,0 1,2 1,4

"dm

"-ef

fect

: The

ta-c

oher

ence

0,0

0,2

0,4

0,6

0,8

1,0

r = 0.80, p = 0.018

Non-specific increase of theta-coherence

Specific theta-gamma

interaction


Gamma activity: interactions with theta and action potentials

Chrobak u. Buzsáki, J. Neurosci. 1998

Interactions(hippocampus):

Theta (5Hz)

Gamma (>30Hz)

Spikes


Hebbian assembly formation

Correlated firing of pre- and postsynaptic neuron Increase of synaptic efficacy (1949)

Experimental validation:

• Long-term potentiation and depression (LTP, LDP)• Spike timing dependent synaptic plasticity (STDP)

Synchronized gamma activity: precise spike timing (t < 10 ms)

(z.B. Engel u. Singer, Trends Cogn. Sci. 2001; Fries et al., Nat. Neurosci. 2001)

Abbott u. Nelson, Nat.. Neurosci. 2000


Rhinal-hippocampal coupling during sleep

• Dreams are difficult to remember

• Unrecognized scene shifts

• Duration severely misestimated

Memory formation during (REM-) sleep reduced

(e.g. Hobson et al., Behav. Brain Sci. 2000)

Sleep recordings in 8 unilateral MTLE patients

(Indirect) electrophysiological correlate?


Rhinal-hippocampal coupling during sleep

Eur. J. Neurosci. 2003; 18: 1711-1716


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WachStadium 1Stadium 2SWS = 3, 4REM

1-4

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Rhinal-hippocampal 40 Hz coherence

Awake

Stage 1

REM

Stage 2

SWS

0.5

0.

Time (hours)0 2 4 6

Eur. J. Neurosci. 2003; 18: 1711-1716


Memory formation during sleep

Direct correlate?

Awakenings from REM sleep: dream recall in 6 patients (good, 79.2%) vs. 6 patients (poor, 6.7%)

• No group differences in daytime memory performance

• Sleep: “spontaneous memory formation”, attention, volition, semantic processing

Core factor of declarative memory formation


EEG power within hippocampusDeclarative memory formation: MTL connectivity

Rhinal-hippocampal EEG coherenceDeclarative memory formation: MTL connectivity

Rhinal-hippocampal connectivity

= core factor of

declarative memory formation

Declarative memory formation: MTL connectivityConclusion

Summary

Quantitative EEG-analysis

•EEG = superposition of functionally specific oscillations

•Averaged ERPs = phase locking + amplitude changes

•Connectivity may be more relevant than amplitudes of local activations

Guillén Fernández

Peter Klaver

Christoph Helmstädter

Thomas Dietl

Rüdiger Köhling

Edgar Kockelmann

Martin Lutz

Wieland Burr

Hakim Elfadil

Mario Städtgen

Carlo Schaller

Christian E. Elger

Kontakt: [email protected]

Dept. of EpileptologyUniversity of Bonn

www.epileptologie-bonn.de

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Quantitative analysis of electroencephalographic (EEG) signals